Field Crops Research最新文献

{"title":"Management options to reduce nitrogen surplus in potato production in the Netherlands; a modelling approach","authors":"H.N.C. Berghuijs ,&nbsp;T. Ten Den ,&nbsp;I. Van de Wiel ,&nbsp;F.K. Van Evert ,&nbsp;A.J.W. De Wit ,&nbsp;M.K. Van Ittersum ,&nbsp;P. Reidsma ,&nbsp;A.P.P. Ravensbergen","doi":"10.1016/j.fcr.2026.110396","DOIUrl":"10.1016/j.fcr.2026.110396","url":null,"abstract":"<div><h3>Context and Objective</h3><div>Nitrogen (N) overapplication leads to N surpluses and possible environmental problems. Overuse may particularly apply to cash crops which receive relatively high amounts of inputs, such as potato. To predict the effects of N fertilization regimes on crop production and to optimize N management, crop growth models can be used. In this study, we calibrated and evaluated the crop growth model WOFOST for potato and explored various management options to reduce N surpluses without yield loss.</div></div><div><h3>Methods</h3><div>WOFOST was calibrated and evaluated on an experimental potato data set conducted over two growing seasons, at two locations, using five cultivars and under a combination of two irrigation regimes and three N fertilization regimes. The calibrated model was then evaluated on data from 94 farmers’ fields throughout the Netherlands. Next, three different management options to reduce N surplus were investigated: 1) close the efficiency yield gap, 2) reduce the N input without yield loss or soil mining, and 3) target 90 % of the water-limited yield without soil mining.</div></div><div><h3>Results and conclusions</h3><div>WOFOST reproduced dry matter production and partitioning, and tuber N amounts from the experiments well. Also, WOFOST simulations matched the tuber dry matter production of farmers’ fields adequately throughout the season. Baseline N surpluses estimates were on average 2.6 times larger than the proposed maximum threshold values. All management options reduced the median N surplus to values slightly above or below these thresholds. However, both soil mining and N surplus above recommended threshold values still occurred in management option 1. In the other two options, it was possible on almost all fields with a clay soil to reach N surpluses below the recommended threshold values. The N surplus was slightly above the threshold value on sandy soils.</div></div><div><h3>Significance</h3><div>Our work on the calibration and evaluation of WOFOST for potato under N-limited growth conditions allows using this model to assess N fertilization regimes and their effects on yield and the environment in potato production. We subsequently did such an analysis on data from Dutch farmers’ fields with potato. Only closing the efficiency yield gap was insufficient to prevent both soil mining and N surpluses above threshold values for more than half of the fields. However, it was possible to adjust the N input levels to either maintain current yields or obtain 90 % of the water-limited yields without soil mining on clay soils. Although adopting these management options also substantially reduced the N surplus for potato grown on sandy soils, the N surplus was still slightly above the recommended threshold value.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110396"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risks of wheat yields reduction under future climate extremes","authors":"Guanmo Li ,&nbsp;Lei Qiao ,&nbsp;Jianzheng Li ,&nbsp;Lei Wu ,&nbsp;Jie Pan ,&nbsp;Minggang Xu ,&nbsp;Wenju Zhang ,&nbsp;Yves Brostaux","doi":"10.1016/j.fcr.2026.110395","DOIUrl":"10.1016/j.fcr.2026.110395","url":null,"abstract":"<div><div>Climate change increasingly threatens global wheat production,with rising temperatures and shifting precipitation patterns reducing yields. Understanding future spatiotemporal patterns of and key climate drivers of wheat yield is critical for sustainable development. However, previous studies often overlooked complex nonlinear relationships among multiple climate factors. Here, we combined APSIM-wheat with Random Forest-SHAP to capture both the wheat growth process and complex, non-linear relationship and threshold effects between yield and climate factors. A comprehensive dataset consisted of 15 national long-term experiments located in three major wheat production regions of China was used for this purpose. APSIM models showed strong predictive accuracy across all regions. Using the validated crop models, we projected future wheat yield and analyzed the response to extreme climate events under various scenarios. Results indicated that wheat production would stagnate or decline in the mid- to long term, varying by climate scenarios and regional condition. Yields ranged from 4.0 to 7.2 t ha⁻¹ under low-emission and 2.5–6.5 t ha⁻¹ under high-emission scenarios. In the long term, the gap between low- and high- emissions scenarios was expected to widen. Under high-emissions scenarios, yields in Northwest China decreased by 30–40 % by 2099 due to heavy precipitation and cold extremes, making it the most vulnerable areas. In Yangtze River Basin, yields decreased by 19–36 % as hot extremes and heavy precipitation, while in North China Plain, they decreased by 17–25 %, mainly from hot extremes. Extreme climate events had significant nonlinear and threshold effects on yields. This study emphasizes the need for greater consideration of region-specific strategies and provides a foundation for targeting management practices to maintain food security and enhance long-term resilience to future multiple climate disasters.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110395"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving plot-level wheat yield prediction by integrating proximal-sensed spike photosynthetic phenotyping into crop growth model","authors":"Haiyu Tao ,&nbsp;Yining Tang ,&nbsp;Jiaoyang He ,&nbsp;Caili Guo ,&nbsp;Xia Yao ,&nbsp;Tao Cheng ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Yongchao Tian","doi":"10.1016/j.fcr.2026.110402","DOIUrl":"10.1016/j.fcr.2026.110402","url":null,"abstract":"<div><h3>Context</h3><div>Crop growth models (CGMs) have been widely employed to simulate crop growth processes and predict grain yield. Although wheat spike photosynthesis has been validated to contribute substantially to yield formation, few existing models explicitly account for this key process. This limitation may restrict a comprehensive understanding of crop growth dynamics and compromises the accuracy of grain yield prediction.</div></div><div><h3>Objective</h3><div>This study aimed to develop an integrated model incorporating spike photosynthesis, validate the accuracy of spike light interception estimation, and evaluate model performance in plot-scale wheat yield prediction.</div></div><div><h3>Method</h3><div>A two-year (2021–2023) field experiment was conducted using 17 wheat varieties under four management practices. Proximal RGB images were combined with deep learning algorithm to estimate the spike light interception ratio (SIR). A novel multi-layered sunlit-shaded canopy photosynthesis module (2M-TPM) was constructed and integrated with SIR into the WheatGrow model, forming the improved WheatGrow-M framework to predict wheat grain yield at the plot level.</div></div><div><h3>Results</h3><div>SIR was estimated with an accuracy exceeding 90 %, and showed strong consistency with field-measured spike radiation interception, with correlation coefficients ranging from 0.78 to 0.83, and varied significantly among wheat varieties and management practices. Spike layers alleviated leaf photosynthetic saturation under high radiation. The WheatGrow-M model reduced the relative root mean square error (RRMSE) of plot-level yield estimation to below 20 %, and revealed that ignoring spike photosynthesis could introduce up to 40 % uncertainty in CGM-based yield prediction.</div></div><div><h3>Conclusion</h3><div>The WheatGrow-M model provides a promising tool for analyzing non-foliar photosynthetic phenotype-yield relationships under genotype-environment-management interactions, and facilitating small-scale yield variation analysis and data assimilation between remote sensing and crop growth models.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110402"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scaling biofortified wheat production for agrifood and nutritional security in eastern India: Evidence from five years of multi-location field evaluations in Bihar","authors":"Ravinder Kumar Giri ,&nbsp;Binu Cherian ,&nbsp;Birendra Mendali ,&nbsp;Parminder Virk ,&nbsp;Wolfgang Pfeifer ,&nbsp;Raj Kumar Jat ,&nbsp;Moti Lal Meena ,&nbsp;Shubham Durgude ,&nbsp;Koushik Banerjee ,&nbsp;Mohammad Hasanain ,&nbsp;Vijay Singh Meena","doi":"10.1016/j.fcr.2026.110393","DOIUrl":"10.1016/j.fcr.2026.110393","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;Micronutrient malnutrition (“&lt;em&gt;hidden hunger&lt;/em&gt;”) remains a persistent agrifood and public health challenge in South Asia. Although cereal-based diets ensure calorie sufficiency, they frequently fail to meet essential micronutrient requirements, particularly zinc and iron. In the eastern Indo-Gangetic Plains (EIGP) of India, Bihar exhibits a high prevalence of anemia, stunting, and underweight populations. Addressing these challenges requires scalable, food-based nutritional interventions that enhance dietary quality without compromising crop productivity, climatic resilience, or farmer incomes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This study tested the hypothesis that zinc-biofortified wheat varieties can simultaneously deliver stable grain yield, enhanced grain zinc and iron concentrations, and broad agro-ecological adaptability under farmer-managed conditions in Bihar.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Multi-location field evaluations were conducted over five consecutive wheat-growing seasons (2018–19–2022–23) across all 14 districts of Bihar, covering diverse agro-climatic sub-zones. Two zinc-biofortified wheat varieties (BHU-25 and BHU-31) were compared with a widely cultivated commercial variety (HD-2967). The dataset comprised more than 11,000 plot-level observations generated through HarvestPlus yield trials, strip trials, and farmer-led demonstrations conducted under real-world management practices.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Across locations and seasons, both biofortified varieties produced grain yields comparable to, or numerically higher than, the commercial check (HD-2967), with mean yields ranging from 3.9 to 4.2 Mg ha⁻¹ . Although BHU-31 recorded a numerically higher mean yield (3.6 %) and BHU-25 a marginal advantage (0.5 %) over HD-2967, these differences were not statistically significant (P &gt; 0.05), indicating the absence of a yield penalty associated with biofortification. Grain zinc concentrations consistently met or exceeded the biofortification target of 38 mg kg⁻¹ and remained stable across environments, with district means ranging from 26.4 to 40.1 mg kg⁻¹ . Grain iron concentrations showed moderate but significant spatial variation across environments (33.2–40.5 mg kg⁻¹). Importantly, during climatically adverse seasons characterized by excessive rainfall and heat stress, both biofortified varieties maintained yields above 3.0 Mg ha⁻¹ , comparable to the commercial check.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Zinc-biofortified wheat varieties demonstrated a rare combination of high yield potential, enhanced micronutrient density, and climatic resilience under farmer-managed conditions, with no evidence of a yield penalty.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Implications and limitations&lt;/h3&gt;&lt;div&gt;The findings provide robust field-scale evidence that zinc-biofortified wheat is a cost-effective, climate-resilient, and nutrition-sensitive intervention for cereal-based food systems. Integratio","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110393"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green manure co-application boosts arid agroecosystem sustainability: Mixed sowing of feed rape and hairy vetch modulates N input and enzymes for optimized wheat production","authors":"Ru Yu ,&nbsp;Jie Zhou ,&nbsp;Fangdi Chang ,&nbsp;Jiashen Song ,&nbsp;Jing Wang ,&nbsp;Xiaobin Li ,&nbsp;Hua Zhang ,&nbsp;Jufeng Cao ,&nbsp;Xiaohong Li ,&nbsp;Hanjiang Liu ,&nbsp;Hongyuan Zhang ,&nbsp;Yuyi Li","doi":"10.1016/j.fcr.2026.110389","DOIUrl":"10.1016/j.fcr.2026.110389","url":null,"abstract":"<div><h3>Context</h3><div>The incorporation of green manure into cropping systems has been widely advocated as a sustainable strategy for enhancing soil fertility and crop productivity, especially for applying green manure mixtures. However, quantitative information regarding these agro-ecological benefits, particularly concerning long-term crop yield, yield stability and soil quality index (SQI), remains limited.</div></div><div><h3>Objective</h3><div>The aim of this study was to investigate the yield performance and yield stability of a wheat-green manure cropping system and to elucidate key underlying soil ecological mechanisms (including C and N inputs, nutrient related enzymes, and soil quality index) through a 9-year field experiment, in order to determine the optimal green manure returning strategy.</div></div><div><h3>Methods</h3><div>Here, a 9-year (2015–2023) field experiment with three green manure cropping systems (feed rape (FR), hairy vetch (HV), and mixed sowing of feed rape and hairy vetch (FR+HV)) and one conventional treatment (wheat fallow after harvest, CK) has been conducted in a typical low fertility farmland (Hetao Irrigation District), to reveal its influences on C and N inputs from green manure biomass, enzyme activities, SQI, wheat yield and yield stability.</div></div><div><h3>Results</h3><div>Our findings revealed that FR, HV and FR+HV achieved superior C and N input efficiencies, with SOC levels increasing by 7.2 %-13.6 %, and total nitrogen (TN) content rising by 8.9 %-11.0 % under the incorporation of green manure, compared to CK. Enzymatic analyses revealed significant activation of nutrient-cycling hydrolases: leucine aminopeptidase and alkaline phosphatase activities increased by 13.8–30.6 % and 6.3–20.9 % across green manure treatments. Notably, FR+HV exhibited synergistic effects on microbial function, elevating β-glucosidase (8.5–9.9 %), β-xylosidase (25.2–40.6 %), cellulase (24.7–50.1 %), and alkaline phosphatase (9.0–20.9 %) activities relative to monoculture treatments and CK. The integrated FR+HV system improved the SQI by 8.53 %-9.85 % compared to other treatments, correlating with yield improvements by 5.1–21.1 %. FR+HV also achieved superior yield stability (17.8 %) higher than CK and optimized yield components-spike density, grains per spike, and 1000-grain weight surpassed all other treatments. Mechanistically, green manure-derived N inputs drove enzymatic activation, forming a critical pathway for yield enhancement.</div></div><div><h3>Conclusions</h3><div>These findings establish that dual-species green manure co-application (feed rape and hairy vetch) regulates belowground nitrogen dynamics to stimulate soil enzyme activity, thereby amplifying wheat productivity.</div></div><div><h3>Implications</h3><div>We propose FR+HV co-incorporation as a scalable agroecological practice for synchronizing soil health and yield sustainability in arid irrigation croplands.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110389"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Model Averaging to Estimate Economic Optimum Nitrogen Rates in Agricultural Crops","authors":"Francisco Palmero ,&nbsp;Ignacio A. Ciampitti ,&nbsp;Trevor J. Hefley","doi":"10.1016/j.fcr.2026.110397","DOIUrl":"10.1016/j.fcr.2026.110397","url":null,"abstract":"<div><h3>Context:</h3><div>Nitrogen (N) is an important yield-limiting factor for crops. Then, N fertilizer input can help to improve (non-legume) crop performance. The N rate balancing crop production and farmers’ incomes is named the Economic Optimum N Rate (EONR). The process of finding the EONR is model-based, yielding different estimations depending on the data and the selected model.</div></div><div><h3>Objectives:</h3><div>The objectives of this research were to: (i) introduce concepts and terminology related to Bayesian inference and Bayesian model averaging (BMA), (ii) evaluate the ability of BMA in estimating the EONR for different experimental designs (data quality) using simulated data, and (iii) apply this technique using data collected in a field experiment.</div></div><div><h3>Methods:</h3><div>We evaluated four response models—quadratic, quadratic-plateau, linear-plateau, and Mitscherlich. A series of simulated datasets was used to assess the performance of BMA relative to individual models. In addition, a case study using an empirical dataset was conducted to illustrate the practical application of BMA in real-world analysis.</div></div><div><h3>Results:</h3><div>In simulation studies, BMA generally performed better than individual models (beyond the model that represents the true data-generating process) in terms of mean sequared error when estimating the EONR. In the real-data case study, the total weight (probability) was mainly applied to one model. Then, the EONR estimated by BMA was close to that of the individual model with the highest weight.</div></div><div><h3>Conclusions:</h3><div>The introduced BMA framework provides researchers with a method for estimating the EONR (or other derived quantities) by considering a set of suitable candidate models.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110397"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanisms of phosphorus acquisition through shifts in root–microbe interactions of Leymus chinensis mediated by nitrogen availability: From synergistic to antagonistic","authors":"Shangpeng Zhang ,&nbsp;Jirui Gong ,&nbsp;Hans Lambers ,&nbsp;Guisen Yang ,&nbsp;Ruijing Wang ,&nbsp;Tong Wang ,&nbsp;Yaohong Yu ,&nbsp;Qin Xie ,&nbsp;Zihe Zhang","doi":"10.1016/j.fcr.2026.110412","DOIUrl":"10.1016/j.fcr.2026.110412","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;Anthropogenic nitrogen (N) deposition disrupts the grassland ecological stoichiometric balance and alters the uptake of phosphorus (P) by grassland plants, thereby affecting the growth of forage crops. However, the specific mechanisms through which soil N availability regulates integrated plant P-acquisition strategies, encompassing root morphology, physiology, and microbial partnerships, are not fully understood.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We conducted a N addition experiment with three levels (Control, N5, N10) in a temperate steppe, using the dominant grass &lt;em&gt;Leymus chinensis&lt;/em&gt; to elucidate these mechanisms. To further investigate whether P availability alters this pattern, we implemented two P treatments (no P and P5 addition) in addition to the above design.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Moderate N addition exhibited the highest P-uptake rate (&lt;em&gt;PUR&lt;/em&gt;), increasing it by 19.4–47.6 % compared to the no‑nitrogen treatment. Synergistic enhancements in &lt;em&gt;PUR&lt;/em&gt; were supported by significant increases in specific root length (16.7–108 %), the stele diameter to cortex thickness ratio (14.2–22.9 %), and root exudation rate (15.1–105 %). N5 increased arbuscular mycorrhizal fungal abundance, but decreased microbial biomass, whereas N5P5 showed the opposite pattern, indicating that P availability influences &lt;em&gt;Leymus chinensis&lt;/em&gt;' selection of microbial partners. In contrast, both N10 and N10P5 treatments reduced root non-structural carbohydrates, leading to decreased relative abundances of Acidobacteriota and Ascomycota while increasing pathogen abundance. This antagonistic relationship not only decreased &lt;em&gt;PUR&lt;/em&gt; but also increased plant disease risk. Under the relatively arid conditions of 2023, &lt;em&gt;PUR&lt;/em&gt; decreased by 55–90 % yet was significantly influenced by N-P interaction, with the environmental stress sharpening plant sensitivity to the N-P balance and amplifying both the synergistic and antagonistic P acquisition strategies mediated by N levels. Critically, soil P availability mediated a fundamental trade-off: under P-deficient conditions, plants adopted an “outsourcing” strategy by investing carbon in AMF symbiosis, resulting in a 26.1–48.3 % increase in colonization rate. Under P-sufficient conditions, they shifted to a “do-it-yourself” strategy, which involved reducing microbial carbon investment and increasing specific root length for direct P uptake.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Leymus chinensis&lt;/em&gt; P-acquisition is governed by a multi-tiered strategy. Moderate N supply mobilizes diverse P-acquisition strategies and plant-microbial synergism, whereas higher N shifts this synergy into antagonism, with detrimental effects on forage growth.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance&lt;/h3&gt;&lt;div&gt;This study highlights the importance of a holistic plant-microbe perspective for predicting grassland responses to global change. Furthermore, grassland managers should improve mo","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110412"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision nutrient management coupled with integrated weed control reduces weed pressure, enhancing the productivity, profitability and energy use indices of upland direct seeded rice","authors":"Smruti Ranjan Padhan ,&nbsp;Sanjay Singh Rathore ,&nbsp;Kapila Shekhawat ,&nbsp;Shiv Mangal Prasad ,&nbsp;Sushmita Saini ,&nbsp;Subhash Babu ,&nbsp;Soumya Ranjan Padhan ,&nbsp;Shubham Anil Durgude","doi":"10.1016/j.fcr.2026.110404","DOIUrl":"10.1016/j.fcr.2026.110404","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;Rice production is increasingly constrained by water scarcity, promoting direct-seeded rice (DSR) as a resource-efficient alternative to puddled transplanted rice in the rainfed Eastern Plateau and Hills region of India. However, severe early-season weed infestation and inefficient nutrient management strongly limit DSR productivity by reducing crop competitiveness and yield potential.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;The study aimed to identify optimal combinations of precision nutrient and weed management strategies to reduce weed pressure, enhance productivity, profitability, and energy-use efficiency in upland DSR over two growing seasons.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;A field experiment was conducted using a split-plot design with three precision nutrient management strategies; recommended dose of fertilizer (RDF), leaf colour chart (LCC), and Nutrient Expert (NE), in main plots, and five weed management options in sub-plots. Principal component analysis (PCA) and structural equation modelling (SEM) employed to assess treatment interactions and key productivity drivers.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;RDF was associated with 17.5–18.5 % higher weed density compared with the LCC. Among weed control strategies, pendimethalin followed by hand weeding (PHW) achieved the highest weed control efficiency (82.9 %), outperforming pendimethalin followed by bispyribac-Na (PBNa) and brown manuring (BM). NE and LCC produced comparable grain yield (GY) and production efficiency (PE); however, PHW following weed flush control significantly increased GY and PE by 8 % and 17 % over PBNa and BM, respectively, across both years. NE significantly enhanced grain nitrogen concentration and uptake by 7.3–15.4 % and 15.7–38.5 %, respectively, over RDF, leading to higher protein content (10.8–15.2 % over RDF and 7.6–11.1 % over LCC) and protein yield (295–324 kg ha⁻¹), albeit with lower protein economic efficiency (3.55–3.78 US$ kg⁻¹). Energy productivity and energy intensity under NE were significantly higher by 15–18 % and 17–19 % over RDF and by 11 % and 8–9 % over LCC, respectively. Moreover, PCA clearly discriminated the treatments, which were consistently associated with higher yield, nutrient uptake, protein yield, economic returns, and energy-use efficiency. The SEM indicated strong positive effects of nutrient uptake and energy-use efficiency and a negative effect of weed pressure on productivity, explaining 97.7 % of its variation and significantly enhancing economic performance.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;Integrating NE-based nutrient management with PHW effectively reduces weed pressure while enhancing productivity, profitability, and energy performance in upland DSR.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance&lt;/h3&gt;&lt;div&gt;This study provides robust evidence for a sustainable and resource-efficient management strategy for upland DSR, supporting improved yield stability, reduced weed competition, and enhanced eco","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"341 ","pages":"Article 110404"},"PeriodicalIF":6.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating multiple crop models and multi-source data in a knowledge-guided deep learning framework for wheat and maize yield forecasting in the Huang-Huai-Hai Plain, China","authors":"Zhikai Cheng,&nbsp;Xiaobo Gu,&nbsp;Yuanling Zhang,&nbsp;Xiaohai Fang,&nbsp;Yang Xu,&nbsp;Shikun Sun,&nbsp;Yadan Du,&nbsp;Huanjie Cai","doi":"10.1016/j.fcr.2026.110372","DOIUrl":"10.1016/j.fcr.2026.110372","url":null,"abstract":"<div><h3>Context</h3><div>Early forecasts of high-resolution (e.g., 1 km × 1 km) crop yields are crucial for ensuring agricultural sustainability, particularly under climate change. Conventional process-based (e.g., crop models) and data-driven (e.g., machine learning) approaches face limitations due to high uncertainty in complex scenarios and insufficient training samples, respectively.</div></div><div><h3>Objective</h3><div>To address these challenges, we developed an improved knowledge-guided deep learning (IKGDL) framework.</div></div><div><h3>Methods</h3><div>The IKGDL considered biophysical knowledge from multiple crop models (AquaCrop, crop-water productivity model; APSIM, Agricultural Production Systems sIMulator; and WOFOST, World Food Studies model) by the pre-training process and introduced additional constraints from remote sensing data (RS) and extreme climatic events (ECE) by the fine-tuning process.</div></div><div><h3>Results and conclusions</h3><div>The results showed that single crop model had high uncertainty caused by the model structure. The application of multiple crop models and active learning provided enough available samples for guiding the IKGDL framework to learn general knowledge about meteorological variables (maximum temperature, minimum temperature, and precipitation; MV) and yields. IKGDL achieved satisfactory yield forecasts approximately two months before crop harvest with low spatial and temporal uncertainties (coefficient of determination of 0.78 and 0.76, the normalized root mean square error of 16.24 % and 18.44 % for wheat and maize, respectively). Interpretive analyses quantified the contribution of multi-source data to yield prediction through the SHapley Additive exPlanation tool, with importance ranked as MV &gt; RS &gt; ECE. Although the contribution of ECE was lower, it could not be ignored due to its catastrophic damage to yields. The IKGDL provided a novel insight into regional crop yield prediction, and its good extensibility offered significant potential for continuous improvement in the future.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"340 ","pages":"Article 110372"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using ethylene combined with green manuring to enhance rice productivity, economic benefit, and energy efficiency in double-rice paddy field","authors":"Jinxin Sun ,&nbsp;Guopeng Zhou ,&nbsp;Danna Chang ,&nbsp;Rui Liu ,&nbsp;Han Liu ,&nbsp;Zhengbo Ma ,&nbsp;Ting Liang ,&nbsp;Jia Liu ,&nbsp;Chunqin Zou ,&nbsp;Weidong Cao","doi":"10.1016/j.fcr.2026.110369","DOIUrl":"10.1016/j.fcr.2026.110369","url":null,"abstract":"<div><h3>Context</h3><div>Green manuring significantly improves grain yield and soil fertility, while it may increase the risk of methane (CH₄) emissions in paddy fields. Therefore, it is necessary to develop strategies for mitigating CH₄ emissions from paddy fields while preserving the inherent benefits of green manuring.</div></div><div><h3>Objective</h3><div>This study aimed to develop cleaner and more sustainable agricultural practices for rice production by incorporating green manure combined with ethylene application.</div></div><div><h3>Methods</h3><div>A 2-year field study involving winter fallow (CK), green manure (GM), green manure combined with ethylene applied in early rice (GE_ef), late rice (GE_lf), and both the double rice seasons (GE_eh/lh), was performed, rice yield, CH₄ emissions, and energy efficiency, etc., were investigated.</div></div><div><h3>Results</h3><div>Green manuring significantly increased the annual rice yield, net income, net energy, and soil organic carbon sequestration rate by 5.7 %-12.0 %, 804–2227 CNY ha⁻¹, 16.4–28.3 GJ ha⁻¹, and 1893–2023 kg CO₂-eq ha⁻¹ yr⁻¹, while also increased CH₄ emissions by 27.8 %-110.2 % and carbon footprint by 6.3 %-44.6 % relative to CK, respectively. The three ethylene treatments significantly decreased the annual CH₄ emissions and carbon footprint by 34.5 %-55.2 % and 26.5 %-45.6 %, compared with GM treatment. Notably, the GE_eh/lh treatment exhibited the highest annual net income and energy use efficiency, while simultaneously achieving the lowest greenhouse gas emissions and carbon footprint. The highest sustainability evaluation index was found in the GE_eh/lh treatment, which was 1.20–1.47 times higher than other treatments.</div></div><div><h3>Conclusions</h3><div>Using ethylene combined with green manuring, especially GE_eh/lh, increases rice productivity, economic benefits, and energy use efficiency while reducing carbon footprint in double-rice paddy fields.</div></div><div><h3>Significance</h3><div>The study provides a potential practice for increasing rice yield while mitigating CH₄ emissions in the double-rice cropping regions in Southern China_.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"340 ","pages":"Article 110369"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}